Richard Nock

Publication Activity (10 Years)

Years Active: 1995-2024
Publications (10 Years): 105

Top Venues

CoRR

ICML

NeurIPS

NIPS

Publications

Mathieu Guillame-Bert, Richard Nock
Boosting gets full Attention for Relational Learning. CoRR (2024)
Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
Tempered Calculus for ML: Application to Hyperbolic Model Embedding. CoRR (2024)
Richard Nock, Yishay Mansour
How to Boost Any Loss Function. CoRR (2024)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures. AAAI (2024)
Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo, Richard Nock, Lalitha Sankar
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading. CoRR (2024)
Richard Nock, Ehsan Amid, Manfred K. Warmuth
Boosting with Tempered Exponential Measures. NeurIPS (2023)
Yishay Mansour, Richard Nock, Robert C. Williamson
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. ICML (2023)
Kevin H. Lam, Christian J. Walder, Spiridon Penev, Richard Nock
LegendreTron: Uprising Proper Multiclass Loss Learning. ICML (2023)
Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar
Smoothly Giving up: Robustness for Simple Models. AISTATS (2023)
Alexander Soen, Hisham Husain, Richard Nock
Fair Densities via Boosting the Sufficient Statistics of Exponential Families. ICML (2023)
Richard Nock, Ehsan Amid, Manfred K. Warmuth
Boosting with Tempered Exponential Measures. CoRR (2023)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
Optimal Transport with Tempered Exponential Measures. CoRR (2023)
Richard Nock, Mathieu Guillame-Bert
Generative Forests. CoRR (2023)
Ehsan Amid, Richard Nock, Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures. AISTATS (2023)
Tyler Sypherd, Nathan Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar
Smoothly Giving up: Robustness for Simple Models. CoRR (2023)
Kevin Lam, Christian J. Walder, Spiridon Penev, Richard Nock
LegendreTron: Uprising Proper Multiclass Loss Learning. CoRR (2023)
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K. Warmuth
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs. CoRR (2023)
Alexander Soen, Ibrahim M. Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
Fair Wrapping for Black-box Predictions. NeurIPS (2022)
Richard Nock, Mathieu Guillame-Bert
Generative Trees: Adversarial and Copycat. ICML (2022)
Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
Fair Wrapping for Black-box Predictions. CoRR (2022)
Ehsan Amid, Richard Nock, Manfred K. Warmuth
Clustering above Exponential Families with Tempered Exponential Measures. CoRR (2022)
Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli
Neural Network Poisson Models for Behavioural and Neural Spike Train Data. ICML (2022)
Richard Nock, Mathieu Guillame-Bert
Generative Trees: Adversarial and Copycat. CoRR (2022)
Yishay Mansour, Richard Nock, Robert C. Williamson
What killed the Convex Booster ? CoRR (2022)
Tyler Sypherd, Richard Nock, Lalitha Sankar
Being Properly Improper. ICML (2022)
Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock
Manifold Learning Benefits GANs. CVPR (2022)
Richard Nock, Tyler Sypherd, Lalitha Sankar
Being Properly Improper. CoRR (2021)
Frank Nielsen, Richard Nock
Computing Statistical Divergences with Sigma Points. GSI (2021)
Yao Ni, Piotr Koniusz, Richard I. Hartley, Richard Nock
Manifold Learning Benefits GANs. CoRR (2021)
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith
Generalised Lipschitz Regularisation Equals Distributional Robustness. ICML (2021)
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
The Impact of Record Linkage on Learning from Feature Partitioned Data. ICML (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Alexander Soen, Hisham Husain, Richard Nock
Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers. CoRR (2020)
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith
Generalised Lipschitz Regularisation Equals Distributional Robustness. CoRR (2020)
Huijun Wu, Chen Wang, Richard Nock, Wei Wang, Jie Yin, Kai Lu, Liming Zhu
SMINT: Toward Interpretable and Robust Model Sharing for Deep Neural Networks. ACM Trans. Web 14 (3) (2020)
Christian J. Walder, Richard Nock
All your loss are belong to Bayes. CoRR (2020)
Christian J. Walder, Richard Nock
All your loss are belong to Bayes. NeurIPS (2020)
Hisham Husain, Borja Balle, Zac Cranko, Richard Nock
Local Differential Privacy for Sampling. AISTATS (2020)
Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
On Modulating the Gradient for Meta-learning. ECCV (8) (2020)
Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
Adaptive Subspaces for Few-Shot Learning. CVPR (2020)
Richard Nock, Aditya Krishna Menon
Supervised learning: no loss no cry. ICML (2020)
Richard Nock, Aditya Krishna Menon
Supervised Learning: No Loss No Cry. CoRR (2020)
Frank Nielsen, Richard Nock
Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family. CoRR (2020)
Richard Nock, Wilko Henecka
Boosted and Differentially Private Ensembles of Decision Trees. CoRR (2020)
Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
Disentangled behavioural representations. NeurIPS (2019)
Hisham Husain, Richard Nock, Robert C. Williamson
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. CoRR (2019)
Richard Nock, Robert C. Williamson
Lossless or Quantized Boosting with Integer Arithmetic. ICML (2019)
Frank Nielsen, Richard Nock
The Bregman Chord Divergence. GSI (2019)
Zac Cranko, Richard Nock
Boosted Density Estimation Remastered. ICML (2019)
Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama
New Tricks for Estimating Gradients of Expectations. CoRR (2019)
Soumava Kumar Roy, Mehrtash Harandi, Richard Nock, Richard I. Hartley
Siamese Networks: The Tale of Two Manifolds. ICCV (2019)
Hisham Husain, Richard Nock, Robert C. Williamson
A Primal-Dual link between GANs and Autoencoders. NeurIPS (2019)
Daniel Filonik, Tian Feng, Ke Sun, Richard Nock, Alex Collins, Tomasz Bednarz
Non-Euclidean Embeddings for Graph Analytics and Visualisation. SIGGRAPH Asia Posters (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder
Monge blunts Bayes: Hardness Results for Adversarial Training. ICML (2019)
Samitha Herath, Mehrtash Harandi, Basura Fernando, Richard Nock
Min-Max Statistical Alignment for Transfer Learning. CVPR (2019)
Kelvin Hsu, Richard Nock, Fabio Ramos
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds. ECML/PKDD (2) (2018)
Frank Nielsen, Richard Nock
The Bregman chord divergence. CoRR (2018)
Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
Representation Learning of Compositional Data. NeurIPS (2018)
Frank Nielsen, Richard Nock
On the Geometry of Mixtures of Prescribed Distributions. ICASSP (2018)
Amir Dezfouli, Edwin V. Bonilla, Richard Nock
Variational Network Inference: Strong and Stable with Concrete Support. ICML (2018)
Kelvin Hsu, Richard Nock, Fabio Ramos
Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds. CoRR (2018)
Leif W. Hanlen, Richard Nock, Hanna Suominen, Neil Bacon
Private Text Classification. CoRR (2018)
Qiongkai Xu, Juyan Zhang, Lizhen Qu, Lexing Xie, Richard Nock
D-PAGE: Diverse Paraphrase Generation. CoRR (2018)
Zac Cranko, Richard Nock
Boosted Density Estimation Remastered. CoRR (2018)
Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock
Lipschitz Networks and Distributional Robustness. CoRR (2018)
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne
Entity Resolution and Federated Learning get a Federated Resolution. CoRR (2018)
Hisham Husain, Zac Cranko, Richard Nock
Integral Privacy for Density Estimation with Approximation Guarantees. CoRR (2018)
Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder
Monge beats Bayes: Hardness Results for Adversarial Training. CoRR (2018)
Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
f-GANs in an Information Geometric Nutshell. NIPS (2017)
Frank Nielsen, Richard Nock
Bregman Divergences from Comparative Convexity. GSI (2017)
Frank Nielsen, Richard Nock
Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means. CoRR (2017)
Richard Nock, Frank Nielsen
Distribution-free Evolvability of Vector Spaces: All it takes is a Generating Set. CoRR (2017)
Frank Nielsen, Richard Nock
On w-mixtures: Finite convex combinations of prescribed component distributions. CoRR (2017)
Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
Tsallis Regularized Optimal Transport and Ecological Inference. AAAI (2017)
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. CVPR (2017)
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR (2017)
Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
f-GANs in an Information Geometric Nutshell. CoRR (2017)
Frank Nielsen, Richard Nock
Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity. IEEE Signal Process. Lett. 24 (8) (2017)
Frank Nielsen, Richard Nock
MaxEnt Upper Bounds for the Differential Entropy of Univariate Continuous Distributions. IEEE Signal Process. Lett. 24 (4) (2017)
Amir Dezfouli, Edwin V. Bonilla, Richard Nock
Semi-parametric Network Structure Discovery Models. CoRR (2017)
Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen
Tsallis Regularized Optimal Transport and Ecological Inference. CoRR (2016)
Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen
k-variates++: more pluses in the k-means++. ICML (2016)
Frank Nielsen, Richard Nock
Patch Matching with Polynomial Exponential Families and Projective Divergences. SISAP (2016)
Richard Nock, Frank Nielsen, Shun-ichi Amari
On Conformal Divergences and Their Population Minimizers. IEEE Trans. Inf. Theory 62 (1) (2016)
Frank Nielsen, Richard Nock
A series of maximum entropy upper bounds of the differential entropy. CoRR (2016)
Richard Nock, Aditya Krishna Menon, Cheng Soon Ong
A scaled Bregman theorem with applications. NIPS (2016)
Frank Nielsen, Richard Nock
Fast $(1+ε)$-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices. CoRR (2016)
Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
Loss factorization, weakly supervised learning and label noise robustness. ICML (2016)
Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
Fast Learning from Distributed Datasets without Entity Matching. IJCAI (2016)
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
Making Neural Networks Robust to Label Noise: a Loss Correction Approach. CoRR (2016)
Frank Nielsen, Boris Muzellec, Richard Nock
Classification with mixtures of curved mahalanobis metrics. ICIP (2016)
Giorgio Patrini, Richard Nock, Stephen Hardy, Tibério S. Caetano
Fast Learning from Distributed Datasets without Entity Matching. CoRR (2016)
Richard Nock, Raphaël Canyasse, Roksana Boreli, Frank Nielsen
k-variates++: more pluses in the k-means++. CoRR (2016)
Richard Nock
On Regularizing Rademacher Observation Losses. NIPS (2016)
Richard Nock, Aditya Krishna Menon, Cheng Soon Ong
A scaled Bregman theorem with applications. CoRR (2016)
Richard Nock, Giorgio Patrini, Finnian Lattimore, Tibério S. Caetano
The Crossover Process: Learnability meets Protection from Inference Attacks. CoRR (2016)
Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
Loss factorization, weakly supervised learning and label noise robustness. CoRR (2016)
Frank Nielsen, Boris Muzellec, Richard Nock
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances. CoRR (2016)
Richard Nock, Wafa Bel Haj Ali, Roberto D'Ambrosio, Frank Nielsen, Michel Barlaud
Gentle Nearest Neighbors Boosting over Proper Scoring Rules. IEEE Trans. Pattern Anal. Mach. Intell. 37 (1) (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

Richard Nock